The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Farmers often must make planting decisions regarding one or more fields based on incomplete information. Generally, the goal of the farmer is to maximize crop yield, crop quality, and/or profits from sales of the crop. Yet it is often unclear which combination of crop types, soil types, weather events, and management practices will lead to the maximization of these values.
Agronomic modeling techniques are often used to model interactions between a crop and the environment. For instance, one agronomic model may be used to simulate a crop's growth based on an amount of nutrients the crops receive. Ideally, by using a large number of accurate models, every interaction that affects a crops growth can be simulated, thereby granting perfect knowledge of yield outcomes when a crop is planted.
Unfortunately, the use of such a large number of models to capture every interaction between the crop and the environment would be computationally expensive. Additionally, the strength of the agronomic model is limited by the knowledge of the person generating the agronomic model. Thus, an agronomic model is unable to account for relationships that are not understood prior to the model's creation.
Neural networks have become increasingly popular in solving various types of problems without requiring relationships to be specified in advance. Generally, neural networks consist of a series of equations, each of which are configured to transform a plurality of different inputs into one or more outputs. As the neural networks are trained, weights are assigned to the series of equations in order to ensure that the neural network produces correct outputs from the inputs. A benefit of neural networks is that they can capture relationships that are not fully understood by the domain experts.
One weakness with neural networks is that they tend to work on a single type of input to produce a single type of output. In the case of agronomic modeling, there are various different types of inputs, including crop type, soil type, weather effects, and management practices, that are relevant to a crop's yield. The different types of inputs may be represented differently, as some inputs, like temperature, vary with time while other inputs, like soil type, vary spatially.
Thus, there is a need for a comprehensive neural network which can interact with various types of agricultural data in order to produce yield outcomes.